Liveblogging Tuesday @ Hypertext’09

30 06 2009

I’m sharing my live notes from Lada Adamic‘s keynote on “The Social Hyperlink” at Hypertext ’09.

In case you have any additions, comments or links that would make my notes more complete / more useful, please leave a comment and fill in the blanks.

Lada starts by telling a story about the different social networks at MIT vs. Stanford, where at MIT fraternaties are well established and play an important role in defining social communities, while at Stanford they are discouraged – each year you have to enter a room lottery that determines with whom you gonna live with in the coming year. This difference can be observed in the social networks among students. But analyzing the relationships between people, and the actions they perform is challenging because of the difficulty of correlation vs. causation. Do two friends buy the same item because they have a social relationship (causation) or do they happen to buy the same item independent of their relation (correlation)?

The Social Hyperlink (how intent spreads through Second Life):

That’s why Lada got interested in Second Life, as in SL it is possible to trace how information (e.g. dance moves, items) spreads along social ties. In many cases, SL maintains information about previous item owners, allowing us to study how items propagate through networks of SL users. The example Lada talked about was gesture transfer among users of second life. Lada presented results from a study analyzing 12.6 mio transfers (where 23% have accurate previous owner info). What you can do with this data is investigate patterns of information spread through the social network.


  • 48% of transfers happen between friends.
  • Cascades among friends are deeper / items are passed along social ties more often (higher precentage of non-leaf nodes)
  • But: adoption over time is weaker in social networks. Lada speculates that a reason for that is that information spread among friends is “niche” information (only relevant to a small group of homogeneous friends)

The next question Lada deals with is whether targeting hubs/early adopters would be a promising strategy to spread information in networks, by dividing the network into early adopters and laggards:

  • early adopters (or Mavens in Gladwell’s terms) were less social (fewer friends than the average)
  • they were also not active in distributing assests, that means that they are not influencers


  • social networks influences adoption
  • niche items get a bigger boost (from social relations)
  • some individuals have more influence than others

User Intent and Social Networks: What I find interesting about this work, particularly the Second Life Case, is that it allows us to study the propagation of intent in social networks. This kind of data enables us to examine how social relations influence what people want. I find this to be an important research question, because intent is generally assumed to be an attribute of individuals rather than a characteristic of social networks as a whole. I think that people tend to prefer believing that their goals are individual and intrinsic, rather than determined(?) by their social network. Studies such as the SL study have the potential to explore this question empirically.

But network analysis can be employed for other aspects of links as well, Lada gives two more examples:

The Knowledge-Exchange Hyperlink:

One of the questions Lada talked about in this context was: What motivates users to answer questions?

From Interviews from Naver: altruism, learning, hobby, business, points

From crawls: filling in the blanks, correcting others

The Trust Hyperlink:

Lada got interested in Couchsurfing as a way to study trust in social networks. (The rationale being that trust is required to let somebody stay in your home.)

The study included 600.000 users, 156.000 surfed or hosted. 55.000 in largest, strongly connected component

Observerations: Overtime, people tend to engage in both surfing and hosting.

Results: direct reciprocity only accounts for 12-18% (surf the couch of the person you have hosted). Generalized reciprocity is at place.People are willing to vouch for people they only knew via couch-surfing. They tend to vouch for fewer couch-surfing friends than best friends, but overall there are  more couch-surfing friends.

My impressions:

I really enjoyed Lada’s keynote, I think the keynote did a great job in motivating and illustrating the potential of network analysis to explore different aspects of linked information on the web. I came across her work many times before in my own research and I’m happy to have had the chance to hear her talk in person.

Next up are my students Christian and Mark who are pitching their posters on “Understanding the Motivation behind Tagging” (Christian Körner) and “Towards Automatically Annotating Textual Resources with Human Intent” (Mark Kröll). Good luck!

Update (Jul 4 2009): Lada’s slides of the talk are available online!

Dynamic presentation adaptation based on user intent classification on Flickr

10 06 2009

Mathias just pointed me to a recent demonstration of their current research on dynamically adapting the user interface of an image-sharing system, in their case,  based on a classification of user intent.

The problem Mathias and his student, Christoph Kofler, are adressing is interesting, and can be described in the following way.

The basic underlying assumption is that in addition to learning more about the content of image-sharing systems, we also need to know more about the users’ intent in order to improve search.

A majority of research on image sharing systems such as flickr has focused on leveraging and improving the utilization of content-specific (e.g. MPEG7) as well as user-generated (e.g. tags) meta-data to better describe the content of photos or images etc. This allows systems to better reflect what a given image is about. However, when searching for content, the intent of users comes into play. Depending on the users’ search intent, only a subset of resources might be relevant. In other words, a successful search result can be considered to be a search result that successfully matches users’ intent with the content available in image sharing systems.

I’d like to give an example of a particular search intent category in image sharing systems where a recognition of user intent would be useful:

A user who wants to download an image for later commercial use (e.g. to include it in marketing material) might only want to retrieve items that specifically allow him to do that. While this data about copyright in principle is available in image-sharing systems (e.g. The Creative Commons licence) in the form of meta data, these systems need the ability to capture and approximate users’ intent in order to map it onto relevant resources. This is where existing search in image-sharing systems has an enormous potential for improvement.

Mathias and his student are interested in the different possible categories of search intent in image-sharing systems, and how they can help to inform search. They are currently developing a taxonomy of search intent in image-sharing systems and they have already developed an early prototype that aims to demonstrate the potential of learning about user intent and using this knowledge to adapt the presentation of search results. While it appears that the prototype is at an early stage, using simple rule-based mechanisms, I think the prototype excellently demonstrates the difficulty and importance of learning more about the users’ search intent in image-sharing systems.

Dynamic presentation adaptation based on user intent classification on Flickr

Other work on user intent in image-sharing systems focuses on, for example, tag intent aiming to study the different reasons why users tag (Ames and Naaman 2007).

Click here to watch the 6 min demonstration video.


Ames, M. and Naaman, M. 2007. Why we tag: motivations for annotation in mobile and online media. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (San Jose, California, USA, April 28 – May 03, 2007). CHI ’07. ACM, New York, NY, 971-980. DOI=

A research study on “Why users tag”

1 06 2009

I’d kindly like to request your help in a current study I am working on:

Please consider participating in the brief survey on “Why do users tag?”. The entire survey should take you no longer than 1-2 minutes to complete (2 questions only!).

More background on this research can be found in a previous post. Your help would be greatly appreciated.

Please click here to take the survey.